Update app.py

app.py

@@ -1,56 +1,78 @@
-
-
+# app.py - Main file for Hugging Face Spaces deployment
 import numpy as np
 import pandas as pd
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
+from transformers import pipeline, AutoTokenizer
 
-# Load a free model from Hugging Face
-model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"  # Small model that works well for simple tasks
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
+# Use a more lightweight approach with the pipeline API
+# This is more memory-efficient for Spaces deployment
+try:
+    # First try to load a lightweight text generation model
+    generator = pipeline('text-generation', 
+                         model="distilgpt2", 
+                         max_length=100)
+    tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
+    print("Using distilGPT2 model")
+except Exception as e:
+    # Fallback to an even smaller model if resources are limited
+    print(f"Error loading distilGPT2: {e}")
+    print("Falling back to smaller model")
+    generator = pipeline('text-generation', 
+                         model="sshleifer/tiny-gpt2", 
+                         max_length=100)
+    tokenizer = AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2")
 
 # Financial knowledge base - simple templates and responses
 financial_templates = {
     "budget": "Here's a simple budget template based on the 50/30/20 rule:\n- 50% for needs (rent, groceries, utilities)\n- 30% for wants (dining out, entertainment)\n- 20% for savings and debt repayment",
-    "emergency_fund": "An emergency fund should ideally cover 3-6 months of expenses. Start with a goal of $1,000, then build from there.",
+    "emergency fund": "An emergency fund should ideally cover 3-6 months of expenses. Start with a goal of $1,000, then build from there.",
     "debt": "Focus on high-interest debt first (like credit cards). Consider the debt avalanche (highest interest first) or debt snowball (smallest balance first) methods.",
     "investing": "For beginners, consider index funds or ETFs for diversification. Time in the market beats timing the market.",
-    "retirement": "Take advantage of employer matches in retirement accounts - it's free money. Start early to benefit from compound interest."
+    "retirement": "Take advantage of employer matches in retirement accounts - it's free money. Start early to benefit from compound interest.",
+    "save money": "To save money, try the 30-day rule (wait 30 days before making non-essential purchases), meal prep to reduce food costs, and use cashback apps for everyday purchases.",
+    "credit score": "To improve your credit score: pay bills on time, reduce credit card balances, don't close old accounts, and check your credit report regularly for errors."
 }
 
 # Define guided chat flow
 def guided_response(user_message, chat_history):
     # Check if we should use a template response
     for key, template in financial_templates.items():
-        if key in user_message.lower():
-            return template
-    
-    # For more general queries, use the AI model
-    prompt = f"""<human>I need financial advice: {user_message}</human>
-<assistant>"""
+        if key.lower() in user_message.lower():
+            return "", chat_history + [[user_message, template]]
     
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(
-        inputs["input_ids"],
-        max_length=512,
-        temperature=0.7,
-        do_sample=True,
-        pad_token_id=tokenizer.eos_token_id
-    )
-    
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    # Extract just the assistant's response
-    if "<assistant>" in response:
-        response = response.split("<assistant>")[1].strip()
+    # For more general queries, use the AI model with a financial prefix
+    try:
+        prompt = f"The following is financial advice: {user_message}\nFinancial advisor:"
+        outputs = generator(prompt, max_length=150, temperature=0.7, num_return_sequences=1)
+        
+        # Extract relevant part of the response
+        response = outputs[0]['generated_text'].replace(prompt, "").strip()
+        # Cleanup and limit response
+        response = response.split('.')[0] + '.' if '.' in response else response
+        
+        # Add disclaimer for AI-generated content
+        response += "\n\n(Note: This is general advice. Please consult a professional financial advisor for specific situations.)"
+        
+    except Exception as e:
+        response = f"I'm sorry, I couldn't generate advice at the moment. Please try asking about budgeting, emergency funds, debt, investing, or retirement."
     
-    return response
+    return "", chat_history + [[user_message, response]]
 
 # Create budget calculator function
 def calculate_budget(monthly_income, housing, utilities, groceries, transportation):
+    if not monthly_income or monthly_income <= 0:
+        return "Please enter a valid monthly income greater than zero."
+    
+    # Convert inputs to float and handle potential None values
+    housing = float(housing or 0)
+    utilities = float(utilities or 0)
+    groceries = float(groceries or 0)
+    transportation = float(transportation or 0)
+    monthly_income = float(monthly_income)
+    
     total_needs = housing + utilities + groceries + transportation
-    needs_percent = (total_needs / monthly_income) * 100
+    needs_percent = (total_needs / monthly_income) * 100 if monthly_income > 0 else 0
     
     available_for_wants = monthly_income * 0.3
     available_for_savings = monthly_income * 0.2
@@ -69,19 +91,28 @@ def calculate_budget(monthly_income, housing, utilities, groceries, transportati
 # Setup Gradio interface with tabs
 with gr.Blocks() as app:
     gr.Markdown("# Financial Advisor Bot")
+    gr.Markdown("Ask questions about personal finance or use the budget calculator.")
     
     with gr.Tab("Chat Advisor"):
         chatbot = gr.Chatbot(height=400)
-        msg = gr.Textbox(label="Ask a question about personal finance")
+        msg = gr.Textbox(label="Ask about budgeting, emergency funds, debt, investing, retirement, etc.")
         clear = gr.Button("Clear")
         
-        def respond(message, chat_history):
-            bot_message = guided_response(message, chat_history)
-            chat_history.append((message, bot_message))
-            return "", chat_history
-        
-        msg.submit(respond, [msg, chatbot], [msg, chatbot])
+        # Set up the chat interface
+        msg.submit(guided_response, [msg, chatbot], [msg, chatbot])
         clear.click(lambda: None, None, chatbot, queue=False)
+        
+        # Add some example prompts to help users
+        gr.Examples(
+            examples=[
+                "How should I create a budget?",
+                "How much should I save for an emergency fund?",
+                "What's the best way to pay off debt?",
+                "How should I start investing?",
+                "How can I save for retirement?"
+            ],
+            inputs=msg
+        )
     
     with gr.Tab("Budget Calculator"):
         gr.Markdown("## 50/30/20 Budget Calculator")
@@ -97,7 +128,7 @@ with gr.Blocks() as app:
             transport = gr.Number(label="Transportation", value=0)
         
         calculate_btn = gr.Button("Calculate Budget")
-        output = gr.Textbox(label="Budget Analysis", lines=10)
+        output = gr.Textbox(label="Budget Analysis", lines=8)
         
         calculate_btn.click(
             calculate_budget, 
@@ -105,5 +136,5 @@ with gr.Blocks() as app:
             outputs=output
         )
 
-# Launch the app in Colab
-app.launch(share=True)  # share=True creates a public link you can share with others
+# Launch the app
+app.launch()